# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.python.ops.special_math_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import tensorflow as tf


def _random_pd_matrix(n, rng):
  """Random postive definite matrix."""
  temp = rng.randn(n, n)
  return temp.dot(temp.T)


class CholeskySolveTest(tf.test.TestCase):
  _use_gpu = False

  def setUp(self):
    self.rng = np.random.RandomState(0)

  def test_works_with_five_different_random_pos_def_matricies(self):
    with self.test_session():
      for n in range(1, 6):
        for np_type in [np.float32, np.float64]:
          matrix = _random_pd_matrix(n, self.rng).astype(np_type)
          chol = tf.cholesky(matrix)
          for k in range(1, 3):
            rhs = self.rng.randn(n, k).astype(np_type)
            x = tf.cholesky_solve(chol, rhs)
            self.assertAllClose(rhs, tf.matmul(matrix, x).eval(), atol=1e-4)


class CholeskySolveGpuTest(CholeskySolveTest):
  _use_gpu = True


class BatchCholeskySolveTest(tf.test.TestCase):
  _use_gpu = False

  def setUp(self):
    self.rng = np.random.RandomState(0)

  def test_works_with_five_different_random_pos_def_matricies(self):
    with self.test_session():
      for n in range(1, 6):
        for np_type, atol in [(np.float32, 0.05), (np.float64, 1e-5)]:
          # Create 2 x n x n matrix
          array = np.array(
              [_random_pd_matrix(n, self.rng), _random_pd_matrix(n, self.rng)]
          ).astype(np_type)
          chol = tf.batch_cholesky(array)
          for k in range(1, 3):
            rhs = self.rng.randn(2, n, k).astype(np_type)
            x = tf.batch_cholesky_solve(chol, rhs)
            self.assertAllClose(
                rhs, tf.batch_matmul(array, x).eval(), atol=atol)


class BatchCholeskySolveGpuTest(BatchCholeskySolveTest):
  _use_gpu = True


if __name__ == '__main__':
  tf.test.main()
